I have a requirement where reads and writes are quite high ( @ 100-500 per-sec ). A document has the following fields : timestamp, unique-docid, content-text, keyword. Average content-text length is ~ 20 bytes, there is only 1 keyword for a given docid.
At runtime, given a query-term ( which could be null ) and a time-interval, I need to find out top-k frequent keywords which contains the query-term ( optional if its null ) in its context-text field within that time-interval. I can purge the data every day, hence no need for me to have more than a days data. I have quite a few options here : Starting with MySQL, NoSQLs ( Cassandra, Mongo, Couch, Riak, Redis ) , Search-Engine based ( lucene/solr ) each having its own pros/cons. In MySQL we can achieve this via : GROUP-BY/COUNT clause In NoSQL I can probably write a map/reduce task to query these numbers. Although I am not very sure about the query response time. Not sure of we can achieve it via lucene/solr OOB. Any suggestions on what would be a good choice for this use case ? -Thanks, prasenjit --------------------------------------------------------------------- To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org For additional commands, e-mail: java-user-h...@lucene.apache.org
